University of Oulu

S. Savazzi, M. Nicoli, M. Bennis, S. Kianoush and L. Barbieri, "Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems," in IEEE Communications Magazine, vol. 59, no. 2, pp. 16-21, February 2021, doi: 10.1109/MCOM.001.2000200

Opportunities of federated learning in connected, cooperative, and automated industrial systems

Saved in:
Author: Savazzi, Stefano1; Nicoli, Monica2; Bennis, Mehdi3;
Organizations: 1Institute of Electronics, Computer and Telecommunication Engineering (IEIIT) of Consiglio Nazionale delle Ricerche (CNR), Milano, Italy
2Politecnico di Milano DIG and DEIB department, Milano, Italy
3Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-05-19


Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable low-laten-cy communications (URLLC) and computing. These networked multi-agent systems require fast, communication-efficient, and distributed machine learning (ML) to provide mission-crit-ical control functionalities. Distributed ML techniques, including federated learning (FL), represent a mushrooming multidisciplinary research area weaving together sensing, communication, and learning. FL enables continual model training in distributed wireless systems: rather than fusing raw data samples at a centralized server, FL leverages a cooperative fusion approach where networked agents, connected via URLLC, act as distributed learners that periodically exchange their locally trained model parameters. This article explores emerging opportunities of FL for the next-generation networked industrial systems. Open problems are discussed, focusing on cooperative driving in connected automated vehicles and collaborative robotics in smart manufacturing.

see all

Series: IEEE communications magazine
ISSN: 0163-6804
ISSN-E: 1558-1896
ISSN-L: 0163-6804
Volume: 59
Issue: 2
Pages: 16 - 21
DOI: 10.1109/MCOM.001.2000200
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work is partially supported by the European Project CHISTERA RadioSense (Big Data and process modelling for the Smart Industry - BDSI) funded by MUR and by the Project BASE5G (Broadband InterfAces and services for Smart Environments enabled by 5G technologies), funded by the Italian Lombardy Regional Government under the grant POR-FESR 2014-2020 ID 1155850.
Copyright information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.